The study examined the challenges community college students face when manually developing an optimal academic plan to transfer to multiple universities. The authors designed a low-fidelity prototype that lists the minimal set of community college courses an optimization algorithm would output based on the user's selected articulation agreements.
The experiment compared the performance of 24 community college transfer students using either the prototype or the standard ASSIST articulation agreement reports to create an optimal academic plan. The results showed that participants using the prototype made significantly fewer optimality mistakes, were faster in creating their plan, and provided higher usability ratings compared to the ASSIST users.
The authors argue that while manual academic planning can be error-prone, an optimization algorithm could potentially help community college students transfer with fewer unnecessary excess credits and improve the overall usability of the academic planning process. However, the authors note that future research is needed to move beyond a proof of value and towards actually implementing an optimization algorithm.
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by David V. Ngu... pada arxiv.org 05-02-2024
https://arxiv.org/pdf/2307.04500.pdfPertanyaan yang Lebih Dalam